1. Topological Inference Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London SPM Course London, May 2014 Many thanks to Justin Chumbley, Tom Nichols and Gareth Barnes for slides

2. Pre- processings General Linear Model Statistical Inference Contrast c Random Field Theory

3. Statistical Parametric Maps mm time mm time frequency fMRI, VBM, M/EEG source reconstruction M/EEG 2D time-frequency M/EEG 2D+t scalp-time M/EEG 1D channel-time time mm

4. Inference at a single voxel Null distribution of test statistic T u Decision rule (threshold) u : determines false positive rate α Null Hypothesis H 0 : zero activation  Choose u to give acceptable α under H 0

5. Multiple tests t  uu t  uu t  uu t  uu t  uu t  uu If we have 100,000 voxels, α =0.05  5,000 false positive voxels. This is clearly undesirable; to correct for this we can define a null hypothesis for a collection of tests. Noise

6. Multiple tests t  uu t  uu t  uu t  uu t  uu t  uu If we have 100,000 voxels, α =0.05  5,000 false positive voxels. This is clearly undesirable; to correct for this we can define a null hypothesis for a collection of tests.

7. Family-Wise Null Hypothesis FWE Use of ‘corrected’ p-value, α =0.1 Use of ‘uncorrected’ p-value, α =0.1 Family-Wise Null Hypothesis: Activation is zero everywhere Family-Wise Null Hypothesis: Activation is zero everywhere If we reject a voxel null hypothesis at any voxel, we reject the family-wise Null hypothesis A FP anywhere in the image gives a Family Wise Error (FWE) Family-Wise Error rate (FWER) = ‘corrected’ p-value

8. Bonferroni correction The Family-Wise Error rate (FWER), α FWE, for a family of N tests follows the inequality: where α is the test-wise error rate. Therefore, to ensure a particular FWER choose: This correction does not require the tests to be independent but becomes very stringent if dependence.

9. Spatial correlations 100 x 100 independent testsSpatially correlated tests (FWHM=10) Bonferroni is too conservative for spatial correlated data. Discrete dataSpatially extended data

10. Random Field Theory  Consider a statistic image as a discretisation of a continuous underlying random field.  Use results from continuous random field theory. lattice representation

11. Topological inference Topological feature: Peak height Topological feature: Peak height space Peak level inference

12. Topological inference Topological feature: Cluster extent Topological feature: Cluster extent space u clus u clus : cluster-forming threshold Cluster level inference

13. Topological inference Topological feature: Number of clusters Topological feature: Number of clusters space u clus u clus : cluster-forming threshold c Set level inference

14. RFT and Euler Characteristic

15. Expected Euler Characteristic 2D Gaussian Random Field Search volume Roughness (1/smoothness) Threshold

16. Smoothness Smoothness parameterised in terms of FWHM: Size of Gaussian kernel required to smooth i.i.d. noise to have same smoothness as observed null (standardized) data. FWHM 1234 24681013579 Eg: 10 voxels, 2.5 FWHM, 4 RESELS The number of resels is similar, but not identical to the number independent observations. Smoothness estimated from spatial derivatives of standardised residuals: Yields an RPV image containing local roughness estimation.

17. Random Field intuition Corrected p-value for statistic value t

18. Random Field: Unified Theory General form for expected Euler characteristic t, F &  2 fields restricted search regions D dimensions  d (u) : d-dimensional EC density of the field – function of dimension and threshold, specific for RF type: E.g. Gaussian RF:  0 (u) = 1-  (u)  1 (u) = (4 ln2) 1/2 exp(-u 2 /2) / (2  )  2 (u) = (4 ln2) u exp(-u 2 /2) / (2  ) 3/2  3 (u) = (4 ln2) 3/2 (u 2 -1) exp(-u 2 /2) / (2  ) 2  4 (u) = (4 ln2) 2 (u 3 -3u) exp(-u 2 /2) / (2  ) 5/2 

19. Peak, cluster and set level inference Peak level test: height of local maxima Cluster level test: spatial extent above u Set level test: number of clusters above u Sensitivity  Regional specificity  : significant at the set level : significant at the cluster level : significant at the peak level L 1 > spatial extent threshold L 2 < spatial extent threshold

20. Random Field Theory  The statistic image is assumed to be a good lattice representation of an underlying continuous stationary random field. Typically, FWHM > 3 voxels (combination of intrinsic and extrinsic smoothing)  Smoothness of the data is unknown and estimated: very precise estimate by pooling over voxels  stationarity assumptions (esp. relevant for cluster size results).  A priori hypothesis about where an activation should be, reduce search volume  Small Volume Correction: mask defined by (probabilistic) anatomical atlases mask defined by separate "functional localisers" mask defined by orthogonal contrasts (spherical) search volume around previously reported coordinates

21. Conclusion  There is a multiple testing problem and corrections have to be applied on p-values (for the volume of interest only (see SVC)).  Inference is made about topological features (peak height, spatial extent, number of clusters). Use results from the Random Field Theory.  Control of FWER (probability of a false positive anywhere in the image) for a space of any dimension and shape.

22. References  Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Comparing functional (PET) images: the assessment of significant change. Journal of Cerebral Blood Flow and Metabolism, 1991..  Worsley KJ, Evans AC, Marrett S, Neelin P. A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism. 1992.  Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC. A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping,1996.  Chumbley J, Worsley KJ, Flandin G, and Friston KJ. Topological FDR for neuroimaging. NeuroImage, 2010.  Kilner J and Friston KJ. Topological inference for EEG and MEG data. Annals of Applied Statistics, 2010.  Bias in a common EEG and MEG statistical analysis and how to avoid it. Kilner J. Clinical Neurophysiology, 2013.